code: def read_meta(self):
      with open(
          os.path.join(self.root_dir, f"transforms_{self.split}.json"),
          "r",
      ) as f:
          self.meta = json.load(f)
  
      w, h = self.img_wh
      self.focal = (
          0.5 * 800 / np.tan(0.5 * self.meta["camera_angle_x"])
      )  # original focal length
      self.focal *= (
          self.img_wh[0] / 800
      )  # modify focal length to match size self.img_wh
  
      # ray directions for all pixels, same for all images (same H, W, focal)
      self.directions = get_ray_directions(
          h, w, [self.focal, self.focal]
      )  # (h, w, 3)
      self.directions = self.directions / torch.norm(
          self.directions, dim=-1, keepdim=True
      )
      self.intrinsics = torch.tensor(
          [[self.focal, 0, w / 2], [0, self.focal, h / 2], [0, 0, 1]]
      ).float()
  
      self.image_paths = []
      self.poses = []
      self.all_rays = []
      self.all_rgbs = []
      self.all_masks = []
      self.all_depth = []
      self.downsample = 1.0
  
      img_eval_interval = (
          1
          if self.N_vis < 0
          else len(self.meta["frames"]) // self.N_vis
      )
      idxs = list(range(0, len(self.meta["frames"]), img_eval_interval))
      for i in tqdm(
          idxs, desc=f"Loading data {self.split} ({len(idxs)})"
      ):  # img_list:#
          frame = self.meta["frames"][i]
          pose = (
              np.array(frame["transform_matrix"]) @ self.blender2opencv
          )
          c2w = torch.FloatTensor(pose)
          self.poses += [c2w]
  
          image_path = os.path.join(
              self.root_dir, f"{frame['file_path']}.png"
          )
          self.image_paths += [image_path]
          img = Image.open(image_path)
  
          if self.downsample != 1.0:
              img = img.resize(self.img_wh, Image.LANCZOS)
          img = self.transform(img)  # (4, h, w)
          img = img.view(4, -1).permute(1, 0)  # (h*w, 4) RGBA
          img = img[:, :3] * img[:, -1:] + (
              1 - img[:, -1:]
          )  # blend A to RGB
          self.all_rgbs += [img]
  
          rays_o, rays_d = get_rays(
              self.directions, c2w
          )  # both (h*w, 3)
          self.all_rays += [torch.cat([rays_o, rays_d], 1)]  # (h*w, 6)
  
      self.poses = torch.stack(self.poses)
      if not self.is_stack:
          self.all_rays = torch.cat(
              self.all_rays, 0
          )  # (len(self.meta['frames])*h*w, 3)
          self.all_rgbs = torch.cat(
              self.all_rgbs, 0
          )  # (len(self.meta['frames])*h*w, 3)
  
      #             self.all_depth = torch.cat(self.all_depth, 0)  # (len(self.meta['frames])*h*w, 3)
      else:
          self.all_rays = torch.stack(
              self.all_rays, 0
          )  # (len(self.meta['frames]),h*w, 3)
          self.all_rgbs = torch.stack(self.all_rgbs, 0).reshape(
              -1, *self.img_wh[::-1], 3
          )
  
file_path: slimmerf/dataLoader/blender.py
imports: import torch, cv2
  from torch.utils.data import Dataset
  import json
  from tqdm import tqdm
  import os
  from PIL import Image
  from torchvision import transforms
  from .ray_utils import *
label_desc: Use image node_id 3 for calling the BlenderDataset obj's underlying member method code with example usage: obj.read_meta() without return types